AbdulAli Ahmadi

Convolutional neural networks usually require a lot of annotated data for object detection. To avoid the need to acquire and annotate these huge amounts of data, few-shot object detection aims to learn from few object instances of new categories in the target domain. This paper provides a summary on some of the most recent state-of-the-art few shot object detection methods based on transfer learning. Then compare their results on two most common datasets for the task of object detection.

Object Detection, Few Shot Object Detection, Transfer Learning, Meta Learning